Retrieval-Augmented Generation Based Nurse Observation Extraction

Kyomin Hwang; Nojun Kwak

doi:10.63317/2hexmrrsvigr

Retrieval-Augmented Generation Based Nurse Observation Extraction

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Abstract

Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.

Anthology ID:: 2026.clinicalnlp-1.8
Volume:: Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026
Month:: May
Year:: 2026
Address:: Palma, Mallorca (Spain)
Editors:: Asma Ben Abacha, Steven Bethard, Danielle Bitterman, Tristan Naumann, Kirk Roberts
Venues:: ClinicalNLP | WS
SIG:
Publisher:: ELRA Language Resources Association (ELRA)
Note:
Pages:: 66–72
Language:
External URL:: https://lrec.elra.info/lrec2026-ws-clinicalnlp-08
DOI:: 10.63317/2hexmrrsvigr
Bibkey:
Cite (ACL):: Kyomin Hwang and Nojun Kwak. 2026. Retrieval-Augmented Generation Based Nurse Observation Extraction. In Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026, pages 66–72, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).
Cite (Informal):: Retrieval-Augmented Generation Based Nurse Observation Extraction (Hwang & Kwak, ClinicalNLP 2026)
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@inproceedings{hwang-kwak-2026-retrieval,
    title = "Retrieval-Augmented Generation Based Nurse Observation Extraction",
    author = "Hwang, Kyomin  and
      Kwak, Nojun",
    editor = "Ben Abacha, Asma  and
      Bethard, Steven  and
      Bitterman, Danielle  and
      Naumann, Tristan  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical {NLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2026.clinicalnlp-1.8/",
    doi = "10.63317/2hexmrrsvigr",
    pages = "66--72",
    abstract = "Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset."
}

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%0 Conference Proceedings
%T Retrieval-Augmented Generation Based Nurse Observation Extraction
%A Hwang, Kyomin
%A Kwak, Nojun
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F hwang-kwak-2026-retrieval
%X Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.
%R 10.63317/2hexmrrsvigr
%U https://aclanthology.org/2026.clinicalnlp-1.8/
%U https://doi.org/10.63317/2hexmrrsvigr
%P 66-72

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Markdown (Informal)

[Retrieval-Augmented Generation Based Nurse Observation Extraction](https://aclanthology.org/2026.clinicalnlp-1.8/) (Hwang & Kwak, ClinicalNLP 2026)

Retrieval-Augmented Generation Based Nurse Observation Extraction (Hwang & Kwak, ClinicalNLP 2026)

ACL

Kyomin Hwang and Nojun Kwak. 2026. Retrieval-Augmented Generation Based Nurse Observation Extraction. In Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026, pages 66–72, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).